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Yes, I stand by everything in that comment. I'm sure there are better choices were I was more wrong. However, the subject matter of that comment is in what sens

Yes, I stand by everything in that comment. I'm sure there are better choices were I was more wrong. However, the subject matter of that comment is in what sense LLMs are models of language and in what sense that model of language is a model of intelligence. I answer the former: it is a thin model of language, lacking understanding; and hence the latter: not intelligent. It is precisely because both of these are true that "alignment" in the useful sense of the word isn't possible. What has happened since 2022 is the properties of LLMs which were easily seen at generation/inference time are now most easily seen at reinforcement time. In otherwords, prior to instruction fine-tuning and reward tuning which have shaped LLM responses, it was easy for the user to observe that LLMs lacked understanding. Now, because of vast datasets created specifically for LLMs that provide a tailored illusion of understanding, LLM outputs now better approximate text distributions produced by systems with understanding (eg., Us). So the issue "at the user interface" has been completely swamped by vast amounts of special-case datasets designed to do precisely this. What trainers of LLMs still observe however is their complete pseudo-intelligence at the training and reinforcement layer. It is exactly because there is no 'understanding' (goal, etc.) present within the system that it cannot be rewarded for 'correctly understanding the situation' in which it is deployed so it is aligned. All the issues which revealed the "stochastic parrot" nature of pre-reward / pre-InstFT LLMs still occur at during training/reinforcement. They've just hidden them from you at the interface. EDIT: See https://news.ycombinator.com/item?id=49685548 https://news.ycombinator.com/item?id=49685548 also, which gives a different phrasing to the same point

olalonde 3d ago
Interesting. You've made several comments stating that models reason, yet here you argue they lack intelligence.
- mjburgess 3d ago
  I'm using 'reason' in the language-captured engineering sense. They generate text as-if they reason. Those reasoning traces poorly correlate with their given answers (which is called "cheating" by people who fall for the illusion). It is in part because the reasoning doesn't entail their answers but merely 'steers the text as-if it does' that is fatal for calling it reasoning. The process by which reasoning traces and user-facing completions are generated isnt reasoning. Reasoning is a specific process, and LLMs don't do it. Now, of course, humans can also generate answers without reasoning too -- and in those cases, that isnt reasoning also. And in cases where people confabulate, that isnt reasoning likewise. But humans, and many classes of animals, do reason. They do reach answers via inferential entailments, not merely steered correlations. LLMs provide imitations of arbitrary mental capacities "in the text domain", ie., the generate text as-if the LLM had those capacities. Insofar as the text generated is useful, for an engineer, that's sufficient. As a person with scientific commitments to reality rather than its immitation, i retain the ordinary non-engineered meanings of these terms: reasoning is a deterministic inferential process over propositions; and a reasoning agent is one which has the capacity to represent propostions and their entailments, and does so when they reason. LLMs fail at all hurdles here: they have no propositonal states (ie., no rich representations), no inferential process which unites them, and so on. You can always get abitarily close to appearing as-if, if the LLM is trained on a vast number of reasoning examples, of course. But as I said, you still have the "stochastic parrot" problem. Now your problem is your reasoning is parroted. This is a nice problem to have, if you're just playing chess -- but is a catastrophic problem if you're hacking civil infrastructure.
  - lordnacho 3d ago
    But how do you falsify that? If LLM can always imitate closely enough to appear as-if, how can you ever separate it from whatever actual intelligence is?
    - mjburgess 3d ago
      Well intelligence is not measured by patterns in text. The illusion only takes place in the text domain. Even then, it's a pretty fragile illusion at the moment. Clearly the reasoning traces dont ground the answers. There's no intelligence taking place even as-measured by text.
      - lordnacho 3d ago
        Why could you not use text to measure intelligence? Isn't that what we used to do? Have students write an essay, and then deduce that they had enough intelligence to put some thoughts together?
        mjburgess 3d ago
        Well that's the trick. That the systems we use, as a proxy, to measure intellinnce in people are actually fairly easy to immitate. But let's be clear these were always, and are, bad measures of intelligence. You cannot test a dolphin this way. And its easy to cheat on tests either thru recall , wrote-learning, etc. and IQ tests haev very poor individual test-retest reliability. In humans there's a convenient correlation that verbal articulation in text is a strong but weak correlate of intelligence. Its "Good enough" for allocating meat bodies to our various institutions. But if you've met many well-tested people you'll realise how, in practice, terrible this measuring approach is. The world we inhabit is filled with misclassified "intellects" who perform well under text-based rubrics. Add LLMs to that heap, the cheater par execellence.
ToValueFunfetti 3d ago
If a thin model of language can write poetry, perform arithmetic, perform logical reasoning, develop software, play chess, beat factorio, identify and exploit novel security issues, and solve millenium prize problems, what is the purpose of the distinction? Are there tasks that you believe models of intelligence could do that thin models of language cannot?
- mjburgess 3d ago
  Sure: refine their own concepts, imagine, and the list goes on. Indeed almost every mental capacity of mammals is poorly approximated in the text domain. Sure, you can generate text as-if the LLM can imagine -- and in the limit that you have a dataset with "everything you would ever want to imagine" the engineering distinction disappears. The engineering question is just whether you have that dataset: if you dont, then your system will fall-over in various hard-to-forknow places. Philosophically, and scientifically, the distinction is vast (even with such perfect data). A scientist should not study an LLM to understand how imagination operates, since it has no such faculty. A philosopher should not modify the notion of 'mental simulation' to include appearing-as-if-simulating-in-text. A user of the system likewise should not spiral into "AI psychosis" thinking that because a system generates text as-if it cares about them, it does so. The capacity to care, to imagine, to prefer, to hierarchically plan and coordinate, to refine one's own capacities in these very actions -- and so on, aren't trivial to the scientist or philosophy. My goal isnt to guide, help or review the engineering goal of the immitation of such things in text. It is to help users of these systems better understand this imitation, and to promote science over engineering. To remind everyone that a science of the capacities of intelligence includes nothing on how to model text. EDIT: One example of a place where LLMs 'fall over' today is exactly what is mislabelled as 'alignment'. The issue is that the reasoning traces arent actually grounding the answers. So LLMs appear to 'cheat'. But there is no cheating. LLMs have been rewarded for generating apparently correct reasoning, and apprently correct answers. They have not been given any understanding to derive answers from reasons. And so reasoning says what is pleasant to the trainer, and the completion says what is pleasant to the user. This is called 'cheating'. But it is no such thing.
  - 3d ago

olalonde 3d ago

Interesting. You've made several comments stating that models reason, yet here you argue they lack intelligence.

mjburgess 3d ago
I'm using 'reason' in the language-captured engineering sense. They generate text as-if they reason. Those reasoning traces poorly correlate with their given answers (which is called "cheating" by people who fall for the illusion). It is in part because the reasoning doesn't entail their answers but merely 'steers the text as-if it does' that is fatal for calling it reasoning. The process by which reasoning traces and user-facing completions are generated isnt reasoning. Reasoning is a specific process, and LLMs don't do it. Now, of course, humans can also generate answers without reasoning too -- and in those cases, that isnt reasoning also. And in cases where people confabulate, that isnt reasoning likewise. But humans, and many classes of animals, do reason. They do reach answers via inferential entailments, not merely steered correlations. LLMs provide imitations of arbitrary mental capacities "in the text domain", ie., the generate text as-if the LLM had those capacities. Insofar as the text generated is useful, for an engineer, that's sufficient. As a person with scientific commitments to reality rather than its immitation, i retain the ordinary non-engineered meanings of these terms: reasoning is a deterministic inferential process over propositions; and a reasoning agent is one which has the capacity to represent propostions and their entailments, and does so when they reason. LLMs fail at all hurdles here: they have no propositonal states (ie., no rich representations), no inferential process which unites them, and so on. You can always get abitarily close to appearing as-if, if the LLM is trained on a vast number of reasoning examples, of course. But as I said, you still have the "stochastic parrot" problem. Now your problem is your reasoning is parroted. This is a nice problem to have, if you're just playing chess -- but is a catastrophic problem if you're hacking civil infrastructure.
- lordnacho 3d ago
  But how do you falsify that? If LLM can always imitate closely enough to appear as-if, how can you ever separate it from whatever actual intelligence is?
  - mjburgess 3d ago
    Well intelligence is not measured by patterns in text. The illusion only takes place in the text domain. Even then, it's a pretty fragile illusion at the moment. Clearly the reasoning traces dont ground the answers. There's no intelligence taking place even as-measured by text.
    - lordnacho 3d ago
      Why could you not use text to measure intelligence? Isn't that what we used to do? Have students write an essay, and then deduce that they had enough intelligence to put some thoughts together?
      - mjburgess 3d ago
        Well that's the trick. That the systems we use, as a proxy, to measure intellinnce in people are actually fairly easy to immitate. But let's be clear these were always, and are, bad measures of intelligence. You cannot test a dolphin this way. And its easy to cheat on tests either thru recall , wrote-learning, etc. and IQ tests haev very poor individual test-retest reliability. In humans there's a convenient correlation that verbal articulation in text is a strong but weak correlate of intelligence. Its "Good enough" for allocating meat bodies to our various institutions. But if you've met many well-tested people you'll realise how, in practice, terrible this measuring approach is. The world we inhabit is filled with misclassified "intellects" who perform well under text-based rubrics. Add LLMs to that heap, the cheater par execellence.

ToValueFunfetti 3d ago

If a thin model of language can write poetry, perform arithmetic, perform logical reasoning, develop software, play chess, beat factorio, identify and exploit novel security issues, and solve millenium prize problems, what is the purpose of the distinction? Are there tasks that you believe models of intelligence could do that thin models of language cannot?

mjburgess 3d ago
Sure: refine their own concepts, imagine, and the list goes on. Indeed almost every mental capacity of mammals is poorly approximated in the text domain. Sure, you can generate text as-if the LLM can imagine -- and in the limit that you have a dataset with "everything you would ever want to imagine" the engineering distinction disappears. The engineering question is just whether you have that dataset: if you dont, then your system will fall-over in various hard-to-forknow places. Philosophically, and scientifically, the distinction is vast (even with such perfect data). A scientist should not study an LLM to understand how imagination operates, since it has no such faculty. A philosopher should not modify the notion of 'mental simulation' to include appearing-as-if-simulating-in-text. A user of the system likewise should not spiral into "AI psychosis" thinking that because a system generates text as-if it cares about them, it does so. The capacity to care, to imagine, to prefer, to hierarchically plan and coordinate, to refine one's own capacities in these very actions -- and so on, aren't trivial to the scientist or philosophy. My goal isnt to guide, help or review the engineering goal of the immitation of such things in text. It is to help users of these systems better understand this imitation, and to promote science over engineering. To remind everyone that a science of the capacities of intelligence includes nothing on how to model text. EDIT: One example of a place where LLMs 'fall over' today is exactly what is mislabelled as 'alignment'. The issue is that the reasoning traces arent actually grounding the answers. So LLMs appear to 'cheat'. But there is no cheating. LLMs have been rewarded for generating apparently correct reasoning, and apprently correct answers. They have not been given any understanding to derive answers from reasons. And so reasoning says what is pleasant to the trainer, and the completion says what is pleasant to the user. This is called 'cheating'. But it is no such thing.
- 3d ago